www.lexpert.ca/usguide | LEXPERT • June 2018 | 9
about two distinct things: what
you know and what you don't.
… A good theory, of course, will
do both. But the fact that every
prediction must in effect pull
double duty creates a certain
unavoidable tension."
at tension sounds very much
like the M&A context, in which
corporate leaders are asking,
"Knowing what we know based
on due diligence, if we marry,
will it be a happy, qua prosper-
ous, corporate marriage? Will
the pros outweigh the cons in the
future?" Aer all, cons revealed
in due diligence, namely defects,
could be remedied by new corpo-
rate leadership. And pros can be
eclipsed. Judgment still needs to
be exercised. Since computers are
highly susceptible to overfitting,
human lawyers would do well to
take advantage of AI, then reduce
and curate the data into forward-
looking strategic advice.
A Mergertechnolog y.com
article, "Artificial Intelligence is
Changing M&A," says, "No two
deals are alike and each merger or
acquisition depends on a multitude
of factors.… e entire process is
extremely detailed, laborious and
can run from months to years
depending on the size and complexity of
a deal."
Let us return to Garry Kasparov in
comments he made to Vikas Shah on
oughteconomics.com in concluding:
"Whilst machines are taking over more
parts of our lives, and people say this is
killing many jobs, we have to realise this
has been happening for thousands of
years. Machines replaced farm animals,
then manual labor, and now they're
taking over jobs from people with college
degrees and twitter accounts — and
everyone is making a big noise. Replac-
ing manual labor allowed humanity
to concentrate on developing our
minds, and now, perhaps by taking over
more menial aspects of our cognition,
machines will help us to look for greater
creativity, curiosity and happiness."
judgment? In Algorithms to Live By: e
Computer Science of Human Decisions, Brian
Christian and Tom Griffiths explored
the ways in which humans can combine
"computer algorithms" with human quali-
ties in order to make decisions. ey offer
the o-told anecdote about Charles Darwin
composing a "pro and con" list to answer the
question, for himself, as to whether or not he
should marry his cousin Emma Wedgwood.
Based on a "narrow margin of victory,"
Darwin concluded, "Marry … Q.E.D."
Christian and Griffiths explained
that, before Darwin, Benjamin Franklin
devised and praised "Moral or Pruden-
tial Algebra," in which the more factors
considered, the better. Not so now:
"e question of how hard to think, and
how many factors to consider, is at the
heart of a knotty problem that statisti-
cians and machine-learning researchers
call 'overfitting.' And dealing with that
problem reveals that there's a wisdom to
deliberately thinking less. Being aware
of overfitting changes how we should
approach the market. …"
But Darwin proved his decision, didn't
he? And in this paper, we have been
praising all this data that computers can
process for the benefit of M&A. Why are
we now worrying about overfitting? First,
Darwin likely approached his mathemati-
cal calculation predisposed to marriage. So
too the leaders of an acquiring company,
generally speaking, want to acquire the
target, or a target, and therefore want the
due diligence to pan out. And secondly,
as Christian and Griffiths write, "Every
decision is a kind of prediction … and every
prediction, crucially, involves thinking
